25 Key words: Danube Delta, agro- biological indices, vegetation analysis, phytocoenological relevés, annual precipitation, Cynodon dactylon. Ključne besede: delta Donave, agrobiološki indeksi, vegetacijska analiza, fitocenološki popisi, letna količina padavin, Cynodon dactylon. Corresponding author: Simona Dumitrița Chirilă E-mail: simonachirilasc@yahoo.com Received: 22. 1. 2024 Accepted: 7. 8. 2024 Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Simona Dumitrița Chirilă1, Silviu Covaliov1, Ștefan Răileanu1, Livia Oana David1, Mihai Doroftei1, Adrian Burada1 & Marius Făgăraș2 Abstract The study investigated the floristic composition of 12 pasture areas in the Danube Delta, and their neighbouring regions, and the relationships between the floristic composition and the environmental variables. The vegetation analysis was carried out based on the mean percentage values corresponding to the scale developed by the Braun-Blanquet. For the syntaxonomic assignment, 50 phytocoenological relevés were made. The relevés were analyzed using Agglomerative Hierarchical Clustering (flexible β algorithm and Bray-Curtis dissimilarity). The relationship between floristic composition and environmental variables was assessed using Detrended Correspondence Analysis (DCA) and Canonical Correlation Analysis (CCA) in CANOCO. Our results showed that the analyzed species are mostly mesoxerophilic, oligo-mesotrophic, and poorly exploited as fodder, with moderate tolerance to grazing and medium anthropogenic influence, predominantly urbanophobic. Numerical analysis identified eight well-defined communities, which correspond to associations described in the taxonomic literature, based on their diagnostic species. The predominant plant association is Hordeo murini- Cynodontetum dactyloni. The variation of the floristic composition is influenced by annual precipitation. Izvleček V raziskavi smo preučili floristično sestavo 12 pašnih območij v delti Donave in sosednjih regijah ter odnos med vrstno sestavo in okoljskimi spremenljivkami. Analizo vegetacije smo naredili s pokrovnimi vrednostmi Braun-Blanquetove skale, spremenjenimi v povprečne odstotne vrednosti. Za sintaksonomsko opredelitev smo naredili 50 vegetacijskih popisov. Popise smo analizirali z združevalnim hierarhičnim grupiranjem (fleksibilni β algoritem in Bray-Curtisov koeficient različnosti). Odnos med floristično sestavo in okoljskimi spremenljivkami smo opredelili s korespondenčno analizo z odstranjenim trendom in s kanonično korelacijsko analizo (CCA) v programu CANOCO. Rezultati so pokazali, da so obravnavane vrste predvsem mezokserofilne, olifo-mezotrofne in imajo slabo hranilno vrednost, z zmerno odpornostjo na pašo in srednjo na antropogeni vpliv, prevladujejo urbanofobne vrste. Z numerično analizo smo dobili osem dobro opredeljenih združb na osnovi značilnih vrst, ki odgovarjajo v literaturi opisanim asociacijam. Prevladuje asociacija Hordeo murini-Cynodontetum dactyloni. Variabilnost v vrstni sestavi je odvisna predvsem od letne količine padavin. DOI: 10.3986/hacq-2025-000524/1 • 2025, 25–40 1 Danube Delta National Institute for Research and Development, Tulcea, Romania. 2 Ovidius University of Constanța, Faculty of Natural Sciences and Agricultural Sciences, Constanța, Romania. 24/1 • 2025, 25–40 26 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Introduction The pastures from the Danube Delta and its neighbor- hood are biodiversity-rich, with species of plants belong- ing to the Fabaceae, Poaceae, Cyperaceae, and Juncaceae families. According to Păcurar & Rotar (2014), these plants show differences in nutritional value and ecologi- cal conditions. On the other hand, Dipsacaceae, Plantagi- naceae, Verbenaceae, and Urticaceae families have lower nutritional values from this point of view. A detailed in- ventory of the species composition and diversity is essen- tial to characterize plant communities in these pastures (Guretzky et al., 2005; Klimek et al., 2007). Knowing the floristic composition is not only important from an eco- logical perspective but also from an economic perspective (Anamo et al., 2023). Such information about natural habitats can influence conservation and natural resource management decisions. For example, a high degree of in- formation on the nutritional value of different plant spe- cies and families may be necessary for the rational use of grasslands as pastures in animal husbandry. There is a fundamental need to know the habitat pref- erences of plant communities to conserve and assess bio- diversity. Vegetation reflects plant adaptations to specific environmental conditions (Sosnowski & Solka, 2019) and habitat preferences. Floristic composition determi- nes all significant aspects of the habitat, thus making it a prerequisite for understanding the variation in biodiver- sity and species richness in these habitats. As Sewale & Mammo (2022) mentioned, this information is neces- sary for obtaining an overview of biological diversity, as it describes a wide range of plant species. Other studies highlighted the floristic richness of the meadow ecosys- tems in the Danube Delta (Strat, 2016). About 300 plant species have been identified in the pastures of the Dan- ube Delta. For example, species from the Poaceae family account for about 30%, Fabaceae family contribute 10%, and other grassland species from different families com- prise the remaining 60%. The pastoral value of the pastures in the Danube Delta is mediocre (Covaliov, 2023), with a low production po- tential of only 3–5 t/ha MV and an average load of 0.3– 0.5 large cattle units per ha (Decision no. 78/2015). The occurrence of the species Carduus nutans L. in pastures significantly reduces the quality of pastures in Mediter- ranean grasslands (Adar et al., 2023). Also, the quality of pastures is disturbed by the occurrence of various changes in land use and land cover (Calota & Patru-Stupariu, 2019). The rapid increase in anthropogenic activities has sig- nificantly pressured the environment and natural eco- systems. In this context, intensive agricultural practices, extensive industrialization, accelerated urbanization, and climate changes have determined profound transforma- tions in natural ecosystems, directly and indirectly affect- ing biodiversity. The impact of these anthropogenic activ- ities on flora and vegetation has been shown in numerous studies (Dregne, 2002; Nakahama et al., 2015; Hussein et al., 2021). This study aims to analyze the relationship between the floristic composition and environmental variables in some pastures in the Danube Delta. The study objectives were (1) the analysis of the floristic composition of pas- tures in the Danube Delta and (2) identifying environ- mental and chemical variables that influence variation in floristic composition. The results of this study will pro- vide a detailed perspective on the floristic composition of the Danube Delta grasslands and contribute to the un- derstanding of how environmental variables can influence the composition and structure of plant communities in these ecosystems. Materials and methods Study area The study was carried out in 2023 in 12 pasture areas from nine localities (Figure 1) from Tulcea County: Chilia Veche, Colina, Iazurile, Isaccea, Mahmudia, Mu- righiol, Niculițel (Niculițel-Saon, Niculițel-Carieră and Niculițel-Ocol), Pardina and Tulcea-Zaghen (Real Rac- ing Equestrian club). Annual precipitation ranges from 390 mm to 460 mm, and annual mean temperatures from 10 °C to 11 °C (Fick & Hijmans, 2017). The eleva- tion varied from 27 m to 231 m. Predominants are gleyic soils, kastanozem, solonchaks, solonetz, and alluvial soils (Florea & Munteanu, 2003). Vegetation sampling and classification For the vegetation analysis, phytocoenological relevés were carried out in each study area. In total, 50 relevés were carried out (including 120 taxa). The size of the plots was 100 m2 for heterogeneity to capture small-scale variations in species compositions. The vegetation was classified using the Agglomerative Hierarchical Cluster- ing method (ß-flexible method, β = - 0.25, and the Bray- Curtis dissimilarity). The data were represented by the percentage values corresponding to the cover-abundance scale elaborated by Braun-Blanquet (1964), from each relevé, adapted according to Borza & Boșcaiu (1965) and Cristea et al. (2004): r (0.05%); + (0.5%); 1 (5%); 2 (17.5%); 3 (37.5%); 4 (62.5%); 5 (87.5%). 24/1 • 2025, 25–40 27 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania clusters obtained, the diagnostic species were identified using IndVal (Indicator Value; Dufrêne & Legendre 1997). From a statistical point of view, a permutation Figure 1: Map of the study areas: A) at the national level; B) at the Danube Delta ROSCI0065 level. Slika 1: Zemljevidi raziskovanega območja: A) na državni ravni; B) na ravni delte Donave ROSCI0065. 25°00'00"E 30°00'00"E20°00'00"E The dendrogram was performed using the GINKGO software of the VEGANA package (Bouxin, 2005). The mean Silhouette index determined the optimal number of clusters (Rousseeuw, 1987). The synoptic table was made in the JUICE version 7.1 (Tichý, 2002). For the 24/1 • 2025, 25–40 28 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania test was used to select only species significantly associated with clusters (de Cáceres and Legendre, 2009). Thus, the threshold of (sqrt) IndVal was set at 0.500 (Legendre & Legendre, 1998). For each diagnostic species, two values were presented: the first is a statistical value, and the sec- ond is a p-value. For example, for cluster 1, the species Cynodon dactylon has a statistical value 0.713. The p-value for C. dactylon is 0.001, which is less than the significance level (alpha) 0.05 (denoted as ***). The map with the ana- lyzed areas was made in the QGIS program version 3.28 (QGIS Development Team, 2022) and edited in Inkscape v1.3.2 (https://inkscape.org/). Nomenclature The nomenclature of plant species follows EURO+MED (2024), the plant associations follow Chifu et al. (2014), and the higher syntaxa follow Mucina et al. (2016). EU- NIS habitats were also identified using the EUNIS-ESy Expert System (Chytrý et al., 2020). Environmental variables Topographic variables (elevation and aspect), climatic var- iables (annual mean temperature and annual mean pre- cipitation, and chemical variables (pH, C, P, and humus) were used for the multivariate analysis. The elevation (m) and aspect were recorded in the field with AndroiTS GPS Test version 1.48 Pro, and annual mean temperature ( °C) and precipitation (mm) were extracted from the World- Clim database (Fick & Hijmans, 2017). In the AndroiTS GPS Test application, the aspect values were represented by degrees (e.g. 90º) and the abbreviation of the cardinal directions (e.g. E – eastern). For chemical data, soil sam- ples were taken from each studied pasture. Afterwards, the total phosphorus – P (mg kg-1) was analyzed using the Egner-Riehm Domingo method according to ISO 11263 (1994). Soil pH according to SR ISO10390 (1998), the total carbon – C (%) concentration according to SR ISO 106940 (1998), and the humus (%) concentration was analyzed achieved according to SR ISO 106940 (1998) and STAS 7184/21-82. The relationship between floristic composition and environmental variables Two methods were used to analyze the relationships be- tween floristic composition and environmental variables: Detrended Correspondence Analysis (DCA) and Ca- nonical Correspondence Analysis (CCA). In this context, DCA analysis was performed to detect floristic gradients, and CCA analysis was applied to quantify the effect of each environmental variable on floristic composition us- ing the Monte Carlo permutation test (9999 iterations). Collinearity between environmental variables taken in the study was determined with the Variance Inflation Factor (VIF). Only variables with a VIF value < 5 were consid- ered (Table 1). Analyzes were performed in the CANO- CO program version 5.1 (ter Braak & Šmilauer, 2018). Table 1: VIF analysis between the variables studied. Tabela 1: Analiza VIF med preučevanimi spremenljivkami. Variable VIF pH 2.136 Humus % 2.153 total P – total phosphorus (mg kg-1) 1.238 BIO12 – annual mean temperature ( °C) 2.279 Elevation (m) 1.852 Aspect 1.042 Analysis of ecological, agronomic indices and anthropogenic impact For this analysis, various indices were used (Păcurar & Rotar, 2014): the demands of plant species for ecological variables (moisture – M, trophicity – T), agronomic va- riables (tolerance to grazing – G, fodder value – FV), and the evaluation of the anthropogenic impact (hemerobia – Hr; urbanophilia – UR). The analysis was carried out in the program RStudio version 2024.04.2+764 (RStudio Team, 2024), using the „radarchart” package (Ashton et al., 2016). The moisture index (M) describes the relationship be- tween soil moisture and the spread of plant species. The scale developed by Ellenberg et al. (1992) was used. The scale has values from 1 (xerophilic) to 10 (ultrahydro- philic). The trophicity index (T) describes the relationship be- tween the supply of soil elements and the spread of plant species. The scale developed by Ellenberg et al. (1992) was used. The scale has values from 1 – oligotrophic (nitroge- nous) to 9 – extremely eutrophic (extremely nitrophilous). The grazing index (G) describes the relationship be- tween the influences of trampling and grazing and the spread of plant species. The scale developed by Briemle & Ellenberg (1994) and Briemle et al. (2002) was used. The scale has values from 1 – intolerant (sensitive) and 9 – extremely tolerant. The fodder value (FV) includes the chemical compo- sition, degree of consumption, palatability, degree of to- xicity, etc. (Păcurar & Rotar, 2014). The scale developed by Briemle (1996) was used. The scale has values from 1 – toxic species to 9 – excellent fodder species (Klapp et al., 1953). 24/1 • 2025, 25–40 29 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania The hemerobia index (Hr) describes the intensity of the anthropogenic influence. The scale developed by Klotz & Kühn (2002) was used, with modifications made by Păcurar & Rotar (2014). The scale has values from 1 (no human intervention – ahemerobe) to 7 (extreme anthro- pic influence – metahemerobe). The urbanophily index (UR) describes to what extent a certain species is (or is not) linked to human settlements. The scale developed by Klotz & Kühn (2002) was used. The scale has values from 1 (urbanophobic) to 5 (urbano- phile). Results Vegetation analysis The floristic composition was identified in three syn- taxonomical classes: Festuco-Brometea, Molinio-Ar- rhenatheretea, and Sisymbrietea. In this context, the vegetation was grouped into three orders, four alli- ances, five plant associations, and two communities. In the Festuco-Brometea class, two plant associations were included. The vegetation height in the analyzed areas is high and dense. The elevation is medium, the soils are weakly alkaline with moderate concentrations of organic carbon and humus. Also, the pastures have a high vegeta- tion cover, and a low diversity, on slopes with a north- northwest and eastern aspect. In the Molinio-Arrhenatheretea class, a plant association and a plant community were included. The vegetation has an average height in pastures with low elevation. The soils are weakly alkaline, rich in organic carbon and humus, and poor in phosphorus. The pastures have a high vegeta- tion cover and diversity, on slopes with a west-southwest and east-southeast aspect. In the Sisymbrietea class, two associations and two plant communities were included, on slopes with north- northeast, north-west and south aspects. The vegetation has an average height, which occurs at higher elevations. The soils are weakly alkaline, rich in organic carbon and humus and poor in phosphorus. Species diversity and vegetation cover are moderate. Syntaxonomic overview of relevés Class.: Festuco-Brometea Br.-Bl. et Tx. ex Soó 1947 Order: Festucetalia valesiacae Soó 1947 All.: Festucion valesiacae Klika 1931 Ass.: Agropyro pectinati-Kochietum prostratae Zoly- omi 1958 Ass.: Bombycilaeno erecti-Bothriochloetum ischaemi (Dihoru 1970) Dihoru et Doniță 1970 Class.: Molinio-Arrhenatheretea Tx. 1937 Order: Potentillo-Polygonetalia avicularis Tx. 1947 All.: Potentillion anserinae Tx. 1947 Ass.: Rorippo austriacae-Agropyretum repentis (Timár 1947) R. Tx. 1950 Community: Rumex crispus-Xanthium spinosum Class.: Sisymbrietea Gutte et Hilbig 1975 Order: Sisymbrietalia sophiae J. Tx. ex Görs 1966 All.: Cannabidion sativae Golub et al. 2012 Ass.: Cynodonto dactyloni-Atriplicetum tataricae Mo- rariu 1943 All.: Sisymbrion officinalis Tx. et al. ex von Rochow 1951 Ass.: Hordeo murini-Cynodontetum dactyloni Fel- földy ex Borhodi 1949 Community: Cynodon dactylon-Xanthium spinosum Community: Anisantha sterilis-Cynodon dactylon EUNIS Habitats V Vegetated man-made habitats V1 Arable land and market gardens V15 Bare-tilled, fallow, or recently abandoned arable land V3 Artificial grasslands and herb-dominated habitats V31 Agriculturally improved, re-seeded, and heav- ily fertilized grassland, including sports fields and grass lawns R Grasslands and lands dominated by forbs, mosses, or lichens R1 Dry grasslands R1B Continental dry grassland (true steppe) R6 Inland salt steppes R65 Continental subsaline alluvial pasture and meadow Cluster analysis of vegetation The cluster analysis results are presented as a dendrogram (Figure 2) and a synoptic table (Supplement E1). Vege- tation was grouped into eight clusters and reflects the syntaxonomic classification described in the literature (Chifu et al., 2014): cluster 1: Hordeo murini-Cynodon- tetum dactyloni association; cluster 2: Agropyro pectinati- Kochietum prostratae association; cluster 3: Cynodonto dactyloni-Atriplicetum tataricae association; cluster 4: Anisantha sterilis-Cynodon dactylon community; cluster 5: Bombycilaeno erecti-Bothriochloetum ischaemi associa- tion; cluster 6: Rorippo austriacae-Agropyretum repentis associations; cluster 7: Cynodon dactylon-Xanthium spi- nosum community; cluster 8: Rumex crispus-Xanthium spinosum community. 24/1 • 2025, 25–40 30 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Cluster 1: Hordeo murini-Cynodontetum dactyloni Structure and composition of plant community: The di- agnostic species is Cynodon dactylon (0.713, 0.001, ***). This cluster has moderate species diversity and high vegetation cover. The species Artemisia santonicum, Teucrium chamaedrys, Setaria viridis, Bromus hordeaceus and Potentilla reptans, had cover from 0.5% to 17.5%. The dominant species is Cynodon dactylon, which has a significant cover, from 62.5% to 87.5% Ecology: The soil is weakly alkaline and rich in organic carbon and humus concentrations, indicating a fertile substrate. Total phosphorus is present in a poor con- centration. The elevation is low, with slight slopes and a southern aspect. Distribution: This cluster included 33 relevés, carried out in pastures from Chilia, Iazurile, Mahmudia, Murighi- ol, Niculițel (Niculițel-Carieră, Niculițel-Ocol, and Niculițel-Saon), Pardina and Tulcea-Zaghen (Tulcea County). Cluster 2: Agropyro pectinati-Kochietum prostratae Structure and composition of plant community: The di- agnostic species is Agropyron cristatum subsp. pectina- tum (0.999, 0.001, ***). The cluster has a lower diver- sity, with a high vegetation cover. Elytrigia repens has a moderate cover, and the dominant species, Agropyron cristatum subsp. pectinatum, has a cover of 87.5%. Ecology: The soil is weakly alkaline, with a moderate or- ganic carbon and humus concentrations. Total phos- phorus is present in a moderate concentration. The elevation is low, with slight slopes and a western aspect. Distribution: In this cluster were four relevés distributed in the Isaccea pastures (Tulcea County). Cluster 3: Cynodonto dactyloni-Atriplicetum tataricae Structure and composition of plant community: The di- agnostic species are Atriplex tatarica (0.996, 0.001, ***) and Artemisia santonicum (0.971, 0.001, ***). Species diversity is low, and the vegetative cover is high. Arte- misia santonicum and Cynodon dactylon has little cover. The dominant species is Atriplex tatarica, with a cover of 87.5%. Ecology: The soil is weakly alkaline and rich in total phos- phorus, organic carbon and humus concentrations. The elevation is low, with slight slopes and a southern as- pect. Distribution: This cluster contains four relevés distribut- ed in the Iazurile and Colina localities (Tulcea County). Cluster 4: Anisantha sterilis-Eragrostis minor community Structure and composition of plant community: The di- agnostic species are Anisantha sterilis (0.998, 0.039, *), Fragaria viridis (0.997, 0.025, *), and Medicago falcata (0.932, 0.05, *). Species diversity is moderate, and there is a high vegetation cover. Fragaria viridis has a Figure 2: Dendrogram of vegetation data obtained from cluster analysis. Slika 2: Dendrogram vegetacijskih popisov, narejen s klastrsko analizo. 24/1 • 2025, 25–40 31 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania little cover, and Cynodon dactylon has a moderate cover. Eragrostis minor and Anisantha sterilis are the dominant species. Ecology: Community occurs at medium elevations with a western aspect. The soil is weakly alkaline and poor in total phosphorus, organic carbon, and humus concen- trations. Distribution: These relevés (two relevés) are distributed in the pastures of Niculițel-Carieră (Tulcea County). Cluster 5: Bombycilaeno erecti-Bothriochloetum ischaemi Structure and composition of plant community: The diagnostic species are Bothriochloa ischaemum (0.996, 0.013, *) and Knautia arvensis (0.920, 0.046, *). The cluster has low diversity and a high vegetation cover. Bromus hordeaceus and Cynodon dactylon have little cov- er, and the dominant species is Bothriochloa ischaemum. Ecology: The soil is weakly alkaline, and poor in organic carbon, humus concentrations, and total phosphorus concentrations. The elevation is low, with an eastern aspect. Distribution: A relevé made in Tulcea-Zaghen (Tulcea County) was included in this cluster. Cluster 6: Rorippo austriacae-Agropyretum repentis Structure and composition of plant community: The diagnostic species is Elytrigia repens (0.974, 0.026, *). Species diversity is moderate, and the vegetation cov- er is low. The dominant species is E. repens, while the other species have a low cover of 0.5%. This cluster is strongly influenced by E. repens, which has a central role in the structure of the plant association. Ecology: The soil is weakly alkaline and poor in organic carbon, humus concentrations, and total phosphorus concentrations. The elevation is low, with a southeast aspect. Cluster No. of species / 100 m2 VEGC (%) C h e m i c a l v a r i a b l e s Topographic variables Climatic variables pH C % Humus % P ELV (m) Asp Annual mean temperature (°C) Annual mean precipitation (mm) 1 12 ± 4.7 83 ± 9.9 7.5 ± 0.4 8.9 ± 3.7 15.4 ± 6.5 2.6 ± 1.3 61 ± 55 S 10.9 ± 0.4 430 ± 24 2 9 94 ± 2.5 7.9 6.9 11.9 4.8 53 ± 8.1 W 10.9 462 3 5 ± 0.9 97 ± 4.6 7.7 ± 0.3 10.9 ± 5 18.8 ± 8.7 4.9 ± 1.2 31 ± 5.3 S 11.2 ± 0.01 407 ± 5.1 4 14 ± 0.7 76 ± 21 7.07 6.2 10.7 2 163 ± 6.3 W 10 462 5 8 88 8.02 2.7 4.7 2.5 47 E 10.9 436 6 13 69 8.03 2.7 4.7 2.5 63 SE 10.9 436 7 19 ± 1.5 84 ± 3.3 7.5 10.6 18.3 1.5 34 S 11.3 398 8 27 ± 1.4 98 ± 0.7 7.5 10.6 18.3 1.5 32 ± 3.5 S 11.3 398 Distribution: This cluster includes one relevé distributed in Tulcea-Zaghen (Tulcea County). Cluster 7: Cynodon dactylon-Xanthium spinosum com- munity Structure and composition of plant community: Species diversity and vegetation cover are high. Artemisia san- tonicum has a low cover and other species such as Cen- taurea iberica, Matricaria chamomilla, Rumex crispus and Xanthium spinosum have moderate covers. Cynodon dactylon is the dominant species. Ecology: The soil is weakly alkaline, rich in organic car- bon and humus concentrations, and poor in total pho- sphorus concentration. The elevation is low, with a southern aspect. Distribution: This cluster includes three relevés distribu- ted in the Carasuhat (Tulcea County). Cluster 8: Rumex crispus-Xanthium spinosum community Structure and composition of plant community: The diagnostic species are Arctium lappa (1.000, 0.001, ***), Argentina anserina (1.000, 0.001, ***), Ranuncu- lus sceleratus (1.000, 0.001, ***), Veronica serpyllifolia (1.000, 0.001, ***), Urtica dioica (0.957, 0.005, **), and Daucus carota (0.932, 0.004, **). This cluster pre- sents the highest diversity and vegetation cover. Polygo- num aviculare has low cover, and other species, such as Centaurea iberica, Cynodon dactylon, Plantago media, and Xanthium spinosum, have moderate cover. Rumex crispus is the dominant species, 62.5%. Ecology: The soil is weakly alkaline, rich in organic carbon and humus concentrations, and poor in total phosphorus concentration. The elevation is low, with a southern aspect. Distribution: In this cluster are two relevés, distributed in the Carasuhat (Tulcea County). Table 2: Values for the studied variables are means ± standard deviations (SD). VEGC – vegetation cover, Annual mean temperature, Annual precipitation, C – organic carbon, P – total phosphorus, ELV – elevation, Asp – aspect (S – south, W – west, E – east, SE – southeast). Tabela 2: Vrednosti preučevanih spremenljivk: srednje vrednosti ± standardni odkloni (SD). VEGC – pokrovna vrednost vegetacije, povprečna letna temperatura, povprečna letna količina padavin, C – organski ogljik, P – skupni forsfor, ELV – nadmorska višina, Asp – lega (S – jug, W – zahod, E – vzhod, SE – jugovzhod). 24/1 • 2025, 25–40 32 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania DCA and CCA analyses The main determinants of the change in floristic compo- sition are identified by indirect gradient analysis. Given that the gradient index of floristic similarity recorded a value of 4.02 (Table 3), applying the unimodal method, namely the Canonical Correspondence Analysis (CCA), was decided. The first axis explained 8.79% of the vari- ability in the species data, while the second axis explained 16.53% of this variation. The DCA diagram highlights how different vegetation types are distributed on the first and second ordination axes. The first DCA axis was correlated with elevation, aspect and annual precipitation. The principal gradient in vegetation composition along the second axis can be explained by positive correlations with variables such as humus and phosphorus concentrations (Figure 3). The left side of the scatter diagram is occupied by relevés of Agropyro pectinati-Kochietum prostratae (red diamond). Rorippo austriacae-Agropyretum repentis (brown right tri- angle) dominates the lower left part of the ordination space, being well differentiated from the other vegetation units. The relevés of Cynodonto dactyloni-Atriplicetum tataricae (blue star), Cynodon dactylon-Xanthium spino- sum community (orange up triangle) and Rumex crispus- Xanthium spinosum community (skyblue box) are found at the opposite end of the ordination diagram. Hordeo murini-Cynodontetum dactyloni relevés are in the middle of the ordination space (green circle). Variable Explains (%) Contribution (%) pseudo-F p-value P(adj) Annual precipitation (mm) 6.2 25.8 3.2 0.002 0.012 Total phosphorus (mg kg-1) 5 20.5 2.6 0.002 0.012 pH 4.6 18.9 2.5 0.002 0.012 Humus % 3.4 13.9 1.9 0.004 0.024 Elevation (m) 3.2 13.1 1.8 0.002 0.012 Aspect (°) 1.9 7.8 1.1 0.39 1 Figure 3: DCA ordination of the eight vegetation clusters: cluster 1: Hordeo murini-Cynodontetum dactyloni – green (circle); cluster 2: Agropyro pectinati-Kochietum prostratae – red (diamond); cluster 3: Cynodonto dactyloni-Atriplicetum tataricae – blue (star); cluster 4: Anisantha sterilis-Eragrostis minor community – purple (square); cluster 5: Bombycilaeno erecti-Bothriochloetum ischaemi – lightgreen (left triangle); cluster 6: Rorippo austriacae-Agropyretum repentis – brown (right triangle); cluster 7: Cynodon dactylon-Xanthium spinosum community – orange (up triangle); cluster 8: Rumex crispus- Xanthium spinosum community – skyblue (box). Slika 3: DCA ordinacija osmih vegetacijskih klastrov: klaster 1: Hordeo murini-Cynodontetum dactyloni – zeleni krožec; klaster 2: Agropyro pectinati-Kochietum prostratae – rdeči diamant; klaster 3: Cynodonto dactyloni-Atriplicetum tataricae – modra zvezda; klaster 4: združba Anisantha sterilis-Eragrostis minor – vijoličasti kvadrat; klaster 5: Bombycilaeno erecti-Bothriochloetum ischaemi – svetlozeleni levi trikotnik; klaster 6: Rorippo austriacae-Agropyretum repentis – rjavi desni trikotnik ; klaster 7: združba Cynodon dactylon- Xanthium spinosum – oranžni zgornji trikotnik; klaster 8: združba Rumex crispus-Xanthium spinosum – svetlomodra škatla. Axis 1 Axis 2 Axis 3 Axis 4 Eigenvalues 0.6187 0.5442 0.2610 0.2015 Explained variation (cumulative) 8.79 16.53 20.24 23.1 Gradient length 4.02 3.51 2.26 2.18 Pseudo-canonical correlation (suppl.) 0.8628 0.7177 0.6117 0.5615 Table 3: Summary of DCA analysis. Tabela 3: Povzetek analize DCA. The Canonical Correspondence Analysis showed that annual precipitation was the most influential environ- mental factor significantly affecting floristic composition in the Danube Delta pastures and neighboring areas, and others were less significant (Table 4). Table 4: Results of CCA ordination of the effect of variables on floristic composition. Tabela 4: Rezultati CCA ordinacije vpliva abiotskih spremenljivk na floristično sestavo. 0 DCA 1 5 0.0 0. 0 D C A 2 D C A 2 4.04.0 Elevation Aspect Humus Phosphorus 24/1 • 2025, 25–40 33 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Agro-biological analysis Most of the clusters include mesoxerophilic species adapted to moderate to dry conditions. The obtained data showed that the clusters are different regarding soil type. Cluster 3, for example, comprises mesophilic spe- cies adapted to wetter conditions, and cluster 8 comprises species that grow on mesotrophic and moderately nutri- ent-rich soils. Clusters 1, 4, 5, 6, and 7 include oligo- mesotrophic species, which grow on nutrient-poor soils. Figure 4: Cyclogram of agro-biological indices of pasture species. Slika 4: Ciklogram agrobioloških indeksov pašniških vrst. 24/1 • 2025, 25–40 34 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Cluster 2 comprises mesotrophic species that grow on moderately nutrient-rich soils. Regarding tolerance to grazing, it varies from moderate to total tolerance for clus- ter 7. The fodder value was low in all clusters obtained. The anthropogenic impact on these species is generally moderate. All clusters include mesohemerobe and beta- euhemerobe species, indicating a significant anthropo- genic impact on the floral composition. The species are mainly urbanophobic, i.e., they spread predominantly outside human settlements. However, Cluster 3 is a par- ticular case, being urban neutral, which suggests that the species in this cluster are more adaptable to human pres- ence. The analyzed pastures are a mixture of natural and anthropogenic conditions. The floristic composition is dominated by species resistant to grazing, with low forage value and influenced by human activities. These charac- teristics require implementing sustainable grazing practic- es and adjusting pasture management to support animal productivity and ecological balance (Figure 4). In the analyzed study, plant species are described as moderately resistant to grazing. These plants have ad- aptations that allow them to tolerate grazing pressure, but they are not completely immune to it. Animals graze these plants only partially, suggesting that grazing is intense enough to affect the vegetation but not severe enough to eliminate sensitive species. Species such as Cynodon dactylon, Potentilla reptans, etc. are resistant to grazing due to their ability to regenerate quickly and tolerate difficult environmental conditions, including grazing pressure. The type of grazing used is described as „semi-extensive pasture with a tillage system”. This sys- tem involves rotating the animals between different plots (paddocks), which allows re-vegetation in areas that are temporarily unused. Discussion The quality of the pastures is given by the floristic com- position of the pastures (Carreira et al., 2023), which highlights a diverse range of plant species. The plant species analyzed in the 12 pastures from Danube Delta are mostly palatable (Chirilă et al., 2024), indicating an interaction between plant availability and animal prefer- ences. A low number of species was recorded in the analyzed relevés. The decrease in the number of species is a conse- quence of grazing. In this case, our study is in line with the moderate disturbance hypothesis (Fox, 1979) accord- ing to which moderate grazing increases species diversity, while intensive grazing decreases species diversity (Her- rero-Jáuregui & Oesterheld, 2018; Wang et al., 2018). Moderate grazing increases species diversity through di- rect consumption of competitive dominant plant species (Grime, 1973; Al-Mufti et al., 1977). At the same time, Rewilding Europe (2021) showed that areas with regular grazing from the Danube Delta have a greater diversity of species compared to areas without grazing, due to the prevention of the dominance of aggressive vegetation. In general, herbivores have an essential role in controlling the richness of plant species (de Bello et al., 2007). In this context, the role of herbivores has become an important issue in the conservation and management of grazing sys- tems (Olff & Ritchie, 1998; Guo, 2004). Grazing influences floristic composition in the studied pastures, favouring species resistant to grazing. Low spe- cies richness in intensively grazed areas suggests pressure is high. Therefore, the floristic composition is dominated by competitive species of rangelands (grazing-resistant). In contrast, in moderately grazed areas, total species in- creased by containing palatable and hardy plants. The an- alyzed pastures are dominated by species such as Cynodon dactylon, Agropyron cristatum subsp. pectinatum, Atriplex tatarica, and Bothriochloa ischaemum. Cynodon dactylon has been reported to be drought tolerant and able to grow in poor soils (Shi et al., 2014). The species C. dactylon can form dominant communities in various ecosystems due to its high adaptability (García et al., 2023). Also, the species Agropyron cristatum subsp. pectinatum and Bothriochloa ischaemum can form dense communities, suppressing other species. This is also supported by the study of Balyan & et al. (1991). Moreover, in our study, weakly alkaline soils, rich in organic carbon and humus, were associated with a greater number of species. Fer- tile and well-drained soils can support diverse and dense plant communities (Tilman et al., 1996). Species such as Rumex crispus and Artemisia austriaca also grow in these soils with high coverage. In contrast, the species Atriplex tatarica (Kochánková & Mandák, 2008) can colonize ruderal habitats. Relationship vegetation - environment The relationship between floristic composition and envi- ronmental variables is complex and influenced by geo- graphic scale, taxonomic level, and study area (Qin et al., 2019). Our study demonstrated that annual precipita- tion, total phosphorus, soil pH, humus concentration, and elevation are the main variables influencing floristic composition variation in the analyzed pastures. Annual precipitation had the most significant impact on these factors. Following other studies (Xu et al., 2016; Xu et al., 2019), bioclimatic variables such as temperature and 24/1 • 2025, 25–40 35 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania precipitation have been identified as the main variables of vegetation growth and distribution. In addition to these variables, interactions between soil chemical variables, such as phosphorus content and pH, and topographical variables have an essential role in shaping plant commu- nities (Birhanu et al., 2021). A moderate number of species were identified in clus- ters 2 and 4, where more significant amounts of precipita- tion were recorded. However, in cluster 8, where a greater number of species was recorded, precipitation was low. These analyzed areas, which contain a large number of species, in some months of the year, are flooded and con- tain underdeveloped species (the height and size of the leaves is small). Also, species in low precipitation areas may have specific adaptations that allow them to tolerate or thrive under water stress conditions. These adaptations may include morphological and physiological changes (Lambers et al., 2008), and the existence of varied eco- logical niches (Begon et al., 2006). The richest phosphorus concentration is in cluster 3, where a high species richness was recorded. Interestingly, cluster 8 includes the highest number of species, but the phosphorus concentration is deficient. Soil phosphorus is essential for plants, involving various processes such as energy transfer and photosynthesis. Furthermore, differ- ent variations in total phosphorus concentration lead to increased or decreased richness, while the lowest phos- phorus concentration can limit plant concentration (Cui et al., 2023). Soil pH is between 7.07 and 8.03. Clusters with higher pH have fewer species. Cluster 8 includes 27 species per relevé, and the pH is weakly alkaline (7.5). In this con- text, our data show that weakly alkaline soils provide the best conditions for plant growth and development, while extreme pH levels limit species diversity. The data are also consistent with the literature (Zelnik & Čarni, 2013). The higher the concentration of humus, the higher the number of species. Soil humus is important for soil fertil- ity and structure. The high humus concentration shows how important the soil can be for plant growth (Cui et al., 2023). Elevation varies between groups. Higher elevation ran- ges include moderate species richness. The relationship between elevation and species diversity is well studied (Lazarina et al., 2019). Körner (2007) further demon- strated that the diversity of species decreases with eleva- tion since resources are limited, and climatic conditions become more adverse. Conversely, higher elevations can determine a great number of species in certain areas where specific microhabitats and local adaptations are present (Rahbek 1995). Conclusions The floristic composition of the analyzed pastures was diverse and related to environmental variables and graz- ing pressure. Therefore, sustainable management of this diversity is essential for maintaining productivity in these ecosystems. Understanding the impact of grazing on flo- ristic composition can lead to developing management strategies to support the area’s livestock productivity and ecological balance. Environmental variables and anthropogenic impact strongly influence floral composition. Annual precipita- tion is a key factor that determines variation in floristic composition. Such results also show the importance of soil conditions influenced by the anthropogenic impact on the diversity and structure of the vegetation. Most plant species are adapted to moderate to dry conditions (meso-xerophilous). Soil trophic varies be- tween oligo-mesotrophic and mesotrophic while grazing tolerance is uniformly average. The species are generally fodder-poor, influenced by human impact (mesohemer- obes and beta-euhemerobes), and tend to be moderately urbanophobic, distributed predominantly outside human settlements. Through the analysis of the studied pastures, several dominant species have been identified that have a central role in the structure of these plant communities. Cynodon dactylon, Agropyron cristatum subsp. pectinatum, Atriplex tatarica and Bothriochloa ischaemum are the predominant species in these pastures. These species are colonizers, dominate different soil types, and adapt to varied envi- ronmental conditions. These soils are weakly alkaline, rich in organic carbon and humus, and poor in phosphorus. ORCID iDs Simona Dumitrița Chirilă  https://orcid.org/0000-0003- 3397-1834 Silviu Covaliov  https://orcid.org/0000-0002-0412-4102 Ștefan Răileanu  https://orcid.org/0000-0003-1225-5476 Livia Oana David  https://orcid.org/0009-0009-0927-8272 Mihai Doroftei  https://orcid.org/0000-0002-8388-087X Adrian Burada  https://orcid.org/0000-0002-6149-6666 Marius Făgăraș  https://orcid.org/0000-0002-0747-3375 24/1 • 2025, 25–40 36 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania References Adar, S., Sternberg, M., Argaman, E., Henkin, Z., Dovrat, G., Zaady, E., & Paz-Kagan, T. (2023). Testing a novel pasture quality index using remote sensing tools in semiarid and Mediterranean grasslands. Agriculture, Ecosystems & Environment, 357, 108674. https://doi.org/10.1016/j.agee.2023.108674. Al-Mufti, M.M., Sydes, C.L., Furness, S.B., Grime, J.P., & Band, S.R. (1977). A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. Journal of Ecology, 65, 759–791. Anamo, A., Mammo, S., & Temesgen, M. (2023). Floristic composition and community analysis of woody species in Hereje Natural Forest, southwest Ethiopia. SN Applied Sciences, 5(1), 48. https://doi.org/10.1007/s42452-022-05265-9. Ashton, D., Porter, S., Downie, N., Linsley, T., & Entriken, W. (2016). Radarchart: Radar chart from ‘Chart.js’. https://CRAN.R- project.org/package=radarchart. Balyan, R.S., Malik, R.K., Panwar, R.S., & Singh, S. (1991). Competitive ability of winter wheat cultivars with wild oat (Avena ludoviciana). Weed Science, 39(2), 154–158. Begon, M., Townsend, C.R., & Harper, J. L. (2006). Ecology: From individuals to ecosystems. Blackwell Publishing. Birhanu, L., Bekele, T., Tesfaw, B., & Demissew, S. (2021). Relationships between topographic factors, soil and plant communities in a dry Afromontane forest patches of Northwestern Ethiopia. PloS one, 16(3), e0247966. https://doi.org/10.1371/journal.pone.0247966. Borza, A., & Boşcaiu, N. (1965). Introducere în studiul covorului vegetal (Introduction to the study of the plant carpet) [in Romanian]. Academia Republicii Populare Române. Bouxin, G. (2005). Ginkgo, a multivariate analysis package. Journal of Vegetation Science, 16, 355–359. https://doi. org/10.1111/j.1654-1103.2005.tb02374.x Braun-Blanquet, J. (1964). Pflanzensoziologie. Grundzüge der Vegetationskunde. Springer. Briemle, G., & Ellenberg, H. (1994). Zur Mahdverträglichkeit von Grünlandpflanzen – Möglichkeiten der praktischen Anwendung von Zeigerwerten. Natur und Landschaft, 69, 139–147. Briemle, G. (1996). Farbatlas Kräuter und Gräser in Feld und Wald, Stuttgart (Hohenheim): Ulmer, ISBN 3-8001-4125-6. Briemle, G., Nitsche, S., & Nitsche, L. (2002). Nutzungswertzahlen für Gefäßpflanzen des Grünlandes. Schriftenreihe für Vegetationskunde, 38, 203–225. Calota, A.M., & Patru-Stupariu, I. (2019). Pasture resilience towards landscape changes: Assessing pastures quality in the context of land-use and land-cover changes in Romania. European Journal of Geography, 10(2), 12–26. Carreira, E., Serrano, J., Lopes de Castro, J., Shahidian, S., & Pereira, A.F. (2023). Montado mediterranean ecosystem (Soil–Pasture–Tree and animals): a review of monitoring technologies and grazing systems. Applied Sciences, 13(10), 6242. https://doi.org/10.3390/app13106242. Chifu, T., Irimia, I., & Zamfirescu, O. (2014). Diversitatea fitosociologică a vegetaţiei României. II. Vegetația erbacee antropizată. A. Vegetația pajiștilor (The phytosociological diversity of Romania’s vegetation. II. Anthropogenic herbaceous vegetation. A. Grassland vegetation) [in Romanian]. Institutul European 659 pp. Chirilă, S.D., Răileanu, Ș., David, L.O., Covaliov, S., & Doroftei, M. (2024). An analysis of plant palatability on pastures of the delta: Case study, Danube Delta area, Romania. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 52(1), 13568–13568. https://doi. org/10.15835/nbha52113568. Chytrý, M., Tichý, L., Hennekens, S.M., …, & Schaminée, J.H.J. (2020). EUNIS Habitat Classification: expert system, characteristic species combinations, and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/ avsc.12519 Covaliov, S. (2023). Resurse vegetale. In: Lupu, G. (2023). Cercetări privind conservarea biodiversității, habitatelor, speciile invazive (non- native), exploatarea sustenabilă a resurselor naturale și implicațiile socio-economice din Rezervația Biosferei Delta Dunării, în contextul schimbărilor climatice, 230 pagini. Raport Faza 4 / decembrie / 2023, al proiectului nr. PN 23 13 01 03 al contractului nr. 35N/2023/MCI, executant: INCDDD - Tulcea. Tulcea, România. Cristea, V., Gafta, D., & Pedrotti, F. (2004). Fitosociologie (Phytosociology) [in Romanian]. Editura Presa Universitară Clujeană. Cui, S., Xiao, Y., Zhou, Y., Wu, P., Cui, L., & Zheng, G. (2023). Variations in diversity, composition, and species interactions of soil microbial community in response to increased N deposition and precipitation intensity in a temperate grassland. Ecological Processes, 12(1), 35. https://doi.org/10.1186/s13717-023-00445-w de Bello, F., Lepš, J., & Sebastià, M.T. (2007). Grazing effects on the species‐area relationship: Variation along a climatic gradient in NE Spain. Journal of Vegetation Science, 18(1), 25–34. https://doi. org/10.1111/j.1654-1103.2007.tb02512.x de Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90, 3566–3574. https://doi.org/10.1890/08-1823.1 Decision no. 78/2015. Decision no. 78/2015 regarding the modification and completion of the Methodological Norms for the application of the provisions of the Government Emergency Ordinance no. 34/2013 regarding the organization, administration and exploitation of permanent meadows and for the amendment and completion of the Land Fund Law no. 18/1991, approved by Government Decision no. 1.064/2013. Data from: https://lege5.ro/Gratuit/gu3dinrwgm/ hotararea-nr-78-2015-privind-modificarea-si-completarea-normelor- metodologice-pentru-aplicarea-prevederilor-ordonantei-de-urgenta- a-guvernului-nr-34-2013-privind-organizarea-administrarea-si- exploatar?pid=74842424#google_vignette. [accessed 2024 July 25]. Dregne, H.E. (2002). Land degradation in the drylands. Arid land research and management, 16(2), 99–132. https://doi.org/10.1080/153249802317304422 Dufrêne, M., & Legendre, P. (1997). Species assemblages and indicator species: the need for a flexible assymmetrical approach. Ecological Monographs 67, 345–366. https://doi.org/10.1890/0012- 9615(1997)067[0345:SAAIST]2.0.CO;2 Ellenberg, H., Weber, H.E., Düll, R., Wirth, V., Werner, W., & Paulissen, D. (1992). Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica, 18, 1–258. EURO+MED (2024). Euro+Med PlantBase is the information resource for Euro-Mediterranean plant diversity. Data from: http:// ww2.bgbm.org/EuroPlusMed [accessed 2023 December 8]. 24/1 • 2025, 25–40 37 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Fick, S.E., & Hijmans, R.J. (2017). Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 14. https://doi.org/10.1002/joc.5086 Florea, N., & Munteanu, I. (2003). Romanian Soil Taxonomy System (SRTS), ICPA, Est-falia Publishing House, Bucharest, 182 p. Fox, J.F. (1979). Intermediate-disturbance hypothesis. Science, 204, 1344–1345. https://doi.org/10.1126/science.204.4399.1344 Frank, A.B., & Ries, R.E. (1990). Effect of soil water, nitrogen, and growing degree-days on morphological development of crested and western wheatgrass. Rangeland Ecology & Management/Journal of Range Management Archives, 43(3), 257–260. García, S., Guido, A., Pezzani, F., & Lattanzi, F.A. (2023). Invasion strategies of Cynodon dactylon: Competitive ability under low‐ nutrient conditions. Austral Ecology, 48(6), 1107–1120. https://doi. org/10.1111/aec.13341 Grime, J. (1973). Competitive Exclusion in Herbaceous Vegetation. Nature, 242, 344–347. https://doi.org/10.1038/242344a0 Guo, Q. (2004). Slow recovery in desert perennial vegetation following prolonged human disturbance. Journal of Vegetation Science, 15(6), 757–762. https://doi.org/10.1111/j.1654-1103.2004.tb02318.x Guretzky, J.A., Moore, K.J., Brummer, E. C., & Burras, C.L. (2005). Species diversity and functional composition of pastures that vary in landscape position and grazing management. Crop Science, 45(1), 282–289. https://doi.org/10.2135/cropsci2005.0282a Herrero‐Jáuregui, C., & Oesterheld, M. (2018). Effects of grazing intensity on plant richness and diversity: A meta‐analysis. Oikos, 127(6), 757–766. https://doi.org/10.1111/oik.04893 Hussein, E.A., Abd El-Ghani, M.M., Hamdy, R.S., & Shalabi, L.F. (2021). Do anthropogenic activities affect floristic diversity and vegetation structure more than natural soil properties in hyper-arid desert environments? Diversity, 13(4), 157. https://doi.org/10.3390/ d13040157 Inkscape 1.2.2 (732a01da63, 2022-12-09). Data from: https:// inkscape.org/. Accessed on 20 June 2023. ISO 11263 (1994). Determination of phosphorus - Spectrometric determination of phosphorus soluble in sodium hydrogen carbonate solution. Data from: https://www.iso.org/standard/19241.html. [accessed 2023 October 11]. Klapp, E., Boeker, P., König, F., & Stählin, A. (1953). Wartzahlen der Grünlandpflanzen. Das Gründland, 2, 38–42. Klimek, S., Hofmann, M., & Isselstein, J. (2007). Plant species rich- ness and composition in managed grasslands: the relative importance of field management and environmental factors. Biological conservation, 134(4), 559–570. https://doi.org/10.1016/j.biocon.2006.09.007 Klotz, S., & Kühn, I. (2002). Indikatoren des anthropogenen Einflusses auf die Vegetation. Schriftenreihe für Vegetationskunde, 38, 241–246. Kochánková, J., & Mandák, B. (2008). Biological flora of Central Europe: Atriplex tatarica L. Perspectives in Plant Ecology, Evolution and Systematics, 10(4), 217–229. https://doi.org/10.1016/j. ppees.2008.08.001 Körner, C. (2007). The use of‚ altitude’ in ecological research. Trends in Ecology & Evolution, 22(11), 569–574. https://doi.org/10.1016/j. tree.2007.09.006 Lambers, H., Chapin III, F. S., & Pons, T. L. (2008). Plant Physiological Ecology. Springer New York. Lazarina, M., Charalampopoulos, A., Psaralexi, M., Krigas, N., Michailidou, D. E., Kallimanis, A. S., & Sgardelis, S. P. (2019). Diversity patterns of different life forms of plants along an elevational gradient in Crete, Greece. Diversity, 11(10), 200. https://doi. org/10.3390/d11100200 Legendre, P., & Legendre, L (1998). Numerical ecology. Second English edition. Elsevier, Amsterdam, The Netherlands. Mucina, L., Bültmann, H., Dierßen, K. …, & Tichý, L. (2016). Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen and algal communities. Applied Vegetation Science, 19, 3–264. https://doi.org/10.1111/avsc.12257 Nakahama, N., Hirasawa, Y., Minato, T., Hasegawa, M., Isagi, Y., & Shiga, T. (2015). Recovery of genetic diversity in threatened plants through use of germinated seeds from herbarium specimens. Plant Ecology, 216, 1635–1647. https://doi.org/10.1007/s11258-015-0547-8 Olff, H., & Ritchie, M.E. (1998). Effects of herbivores on grassland plant diversity. Trends in ecology & evolution, 13(7), 261–265. https:// doi.org/10.1016/S0169-5347(98)01364-0 Wang, L., Gan, Y., Wiesmeier, M., Zhao, G., Zhang, R., Han, G., Siddique, K.H.M., & Hou, F. (2018). Grazing exclusion-An effective approach for naturally restoring degraded grasslands in Northern China. Land Degradation & Development, 29, 4439–4456. https://doi. org/10.1002/ldr.3191 Păcurar, F., & Rotar, I., (2014). Metode de studiu și interpretare a vegetației pajiștilor, Editura Risoprint, Cluj Napoca. QGIS Development Team (2022). QGIS version 3.28. Data from: https://qgis.org, [accessed 2024 June 29]. Qin, G.X., Wu, J., Li, C.B., Qin, A.N., & Yao, X.Q. (2019). Grassland vegetation phenology change and its response to climate changes in North China. Chinese Journal of Applied Ecology, 30(12), 4099–4107. https://doi.org/10.13287/j.1001-9332.201912.015. Rahbek, C. (1995). The elevational gradient of species richness: a uniform pattern? Ecography, 18(2), 200–205. Rewilding Europe (2021). Rewilding in Action: How Natural Grazing Affects Vegetation in the Danube Delta. URL: https://rewilding- danube-delta.com/news/rewilding-in-action-how-natural-grazing- affects-vegetation-in-the-danube-delta/. Rousseeuw, P.J. (1987). Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi. org/10.1016/0377-0427(87)90125-7 RStudio Team (2024). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. Data from: http://www.rstudio.com/. [accessed 2024 June 29]. Sewale, B., & Mammo, S. (2022). Analysis of floristic composition and plant community types in Kenech Natural Forest, Kaffa Zone, Ethiopia. Trees, Forests and People, 7, 100170. https://doi. org/10.1016/j.tfp.2021.100170 Shi, H., Wang, Y., Cheng, Z., Ye, T., & Chan, Z. (2012). Analysis of natural variation in bermudagrass (Cynodon dactylon) reveals physiological responses underlying drought tolerance. PLoS One, 7(12), e53422. https://doi.org/10.1371/journal.pone.0053422 24/1 • 2025, 25–40 38 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Supplement 1. Synoptic table with the percentage frequencies of plant species in the analyzed communities. Clusters are: cluster 1: Hordeo murini-Cynodontetum dactyloni association; cluster 2: Agropyro pectinati-Kochietum prostratae association; cluster 3: Cynodonto dactyloni-Atriplicetum tataricae association; cluster 4: Anisantha sterilis-Cynodon dactylon community; cluster 5: Bombycilaeno erecti-Bothriochloetum ischaemi association; cluster 6: Rorippo austriacae-Agropyretum repentis associations; cluster 7: Cynodon dactylon-Xanthium spinosum community; cluster 8: Rumex crispus-Xanthium spinosum community. Group (cluster)  1 2 3 4 5 6 7 8 No. of relevés 33 4 4 2 1 1 3 2 Abutilon theophrasti Medik. . . . . . . 33 50 Achillea setacea Waldst. & Kit. 36 100 . 100 . . . . Agrimonia eupatoria L. 33 . . 50 . 100 . . Agropyron cristatum subsp. pectinatum (M. Bieb.) Tzvelev 9 100 . . . . . . Alisma plantago-aquatica L. 3 . . . . . . . Amaranthus retroflexus L. 9 25 . 50 . . 33 . Ambrosia artemisiifolia L. . . . . . . 33 . Anisantha sterilis (L.) Nevski 6 50 . 50 . . . . Arctium lappa L. . . . . . . . 100 Argentina anserina (L.) Rydb. . . . . . . . 100 Artemisia annua L. 3 . . . . . 67 100 Artemisia santonicum L. 24 50 75 . . 100 100 100 Artemisia austriaca Jacq. 6 . 100 . . . . . Artemisia vulgaris L. 3 . . . . . . . Atriplex tatarica L. 6 . 100 . . . . . Ballota nigra L. 18 . . 100 . 100 . . Berteroa incana (L.) DC. 12 . . 50 . 100 . 50 Bidens tripartitus L. 3 . . . . . . 50 Bothriochloa ischaemum (L.) Keng 9 . . . 100 . . . Brassica rapa (L.) L. . . . . . . 67 . Bromus hordeaceus L. 9 25 . . 100 . . . Bromus squarrosus L. 3 . . . . . . . Carduus acanthoides L. 15 25 . . . . . 50 Carduus nutans L. 9 . . . . 100 33 . Sosnowski, J., & Solka, K.M. (2019). Floristic composition of selected lowland meadows in the liw commune. Journal of Ecological Engineering, 20(2), 79-86. https://doi.org/10.12911/22998993/95096 SR ISO 10390 (1998). Soil, treated biowaste and sludge – Determination of pH. SR ISO 10694 (1998). Soil quality – Determination of organic and total carbon after dry combustion (elementary analysis) STAS 7184/21-82. Determination of humus content Strat, D. (2016). Floristic composition and functional zones pattern of the beach-dune system along the Danube Delta coast-Romania. Forum geografic, 15(1), 65–79. https://doi.org/10.5775/fg.2016.093.i ter Braak, C.J.F., & Šmilauer, P. (2018). Canoco reference manual and user’s guide: Software for ordination (version 5.10). Ithaca, USA: Microcomputer Power. Data from: http://www.microcomputerpower. com/. [accessed 2024 June 27]. Tichý, L. (2002). JUICE, software for vegetation classification. Journal of Vegetation Science, 13, 451–453. https://doi. org/10.1111/j.1654-1103.2002.tb02069.x Tilman, D., Wedin, D., & Knops, J. (1996). Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature, 379(6567), 718–720. https://doi.org/10.1038/379718a0 Xu, X., Du, H., Fan, W., Hu, J., Mao, F., & Dong, H. (2019). Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. Journal of Environmental Management, 246, 605–616. https://doi.org/10.1016/j. jenvman.2019.06.023. Xu, Y., Yang, J., & Chen, Y.N. (2016). NDVI-based vegetation responses to climate change in an arid area of China. Theoretical and Applied Climatology, 126, 213–222. https://doi.org/10.1007/s00704- 015-1572-1 Zelnik, I., & Čarni, A. (2013). Plant species diversity and composition of wet grasslands in relation to environmental factors. Biodiversity and Conservation, 22, 2179–2192. https://doi.org/10.1007/s10531-013- 0448-x 24/1 • 2025, 25–40 39 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Group (cluster)  1 2 3 4 5 6 7 8 No. of relevés 33 4 4 2 1 1 3 2 Carex distans L. 3 . . . . . . . Centaurea iberica L. 3 . . . . . 100 100 Centaurea diffusa Lam. 6 . . . . . . . Centaurea orientalis L. 3 . . . . . . . Chenopodium album L. 27 . . . . . . . Cichorium intybus L. 33 . 50 100 100 . 67 . Cirsium arvense (L.) Scop. 6 . . . . . . . Cirsium vulgare (Savi) Ten. 15 . . . . . 33 . Clinopodium vulgare L. 6 . . . . . . . Consolida regalis Gray 6 . . . . 100 . . Convolvulus arvensis L. 45 . . 50 . 100 100 100 Crataegus monogyna Jacq. 3 . . . . . . . Cruciata laevipes Opiz 3 . . . . . . . Cynodon dactylon (L.) Pers. 100 100 100 100 100 100 100 100 Dactylis glomerata L. . 25 . 50 . . . . Datura stramonium L. 3 . . . . . 33 50 Daucus carota L. 15 . . . . . . 100 Dipsacus laciniatus L. 3 . . . . . . . Echinops ritro subsp. ruthenicus (M. Bieb.) Nyman . 75 . . . . . . Echium italicum subsp. biebersteinii (Lacaita) Greuter & Burdet 3 . . . . . . . Echium vulgare L. 3 . . . . . 33 . Elytrigia repens (L.) Nevski 12 50 . . . 100 . . Eragrostis minor Host 82 100 100 100 100 . 33 50 Erodium cicutarium (L.) L’Hér. . . . . . . 67 . Eryngium campestre L. 12 . . 50 . 100 . . Euphorbia seguieriana Neck. 9 . . . . . . . Festuca valesiaca Gaudin 3 25 . 50 . . . . Fragaria viridis Weston 6 . . 50 . . . . Galium humifusum M. Bieb. 48 25 . . . . . . Geranium sanguineum L. 3 . . . . . . . Heliotropium europaeum L. 6 . . 50 . . . . Hordeum murinum L. 9 . . . . . . . Hypericum elegans Willd. 3 . . . . . . . Juncus gerardi Loisel. 12 . . . . 100 . . Juncus conglomeratus L. 3 . . . . . . . Juncus littoralis C. A. Mey. 6 . . . . . . . Knautia arvensis (L.) DC. 18 . . . 100 . . . Lactuca serriola L. 3 25 . . . . . 100 Lamium purpureum L. 3 . . . . . . . Leonurus cardiaca L. . . . 50 . . . . Lepidium ruderale L. 12 . . . . . . . Linaria genistifolia (L.) Mill. . . . . 100 . . . Linum austriacum L. 3 . . . . . . . Lycopus europaeus L. 3 . . . . . . . Malva neglecta Wallr. 3 . . . . . . 50 Malva sylvestris L. 6 . . 50 . . . . Marrubium peregrinum L. 9 100 . . . 100 . . Marrubium vulgare L. 6 . . . . . 33 . Matricaria chamomilla L. . . . . . . 100 100 Medicago falcata L. 15 . . 50 . . . . 24/1 • 2025, 25–40 40 Chirilă et al. Eco-coenotic analysis of pasture areas from the Danube Delta, Romania Group (cluster)  1 2 3 4 5 6 7 8 No. of relevés 33 4 4 2 1 1 3 2 Melilotus officinalis (L.) Lam. 6 . . . . . . . Mentha pulegium L. 15 . . . . . . . Mentha longifolia (L.) L. 9 . . . . . . . Mentha aquatica L. 3 . . . . . . . Ochlopoa annua (L.) H. Scholz 6 . . . . . . . Onopordum acanthium L. 21 . . . . . 67 100 Origanum vulgare L. 3 . . . . . . . Orlaya grandiflora (L.) Hoffm. 3 . . . . . . . Phragmites australis (Cav.) Steud. 3 . 25 . . . . . Picris hieracioides L. 12 . . . . . . . Plantago lanceolata L. 9 . . . . . . . Plantago major L. 3 . . . . . 33 . Plantago media L. 6 . . . . . . 50 Polygonum aviculare L. 42 . . . . . 67 100 Potentilla argentea L. 3 . . 50 . . . . Potentilla reptans L. 24 . . . . . 33 50 Ranunculus repens L. 3 . . . . . . . Ranunculus sceleratus L. . . . . . . . 100 Raphanus raphanistrum L. 3 . . . . . . . Reseda lutea L. 3 . . . . . . . Robinia pseudoacacia L. 3 . . . . . . . Rorippa sylvestris (L.) Besser . . . . . . 100 100 Rosa canina L. 12 . . . 100 100 . . Rubus caesius L. 3 . . . . . . . Rumex crispus L. 6 . . . . . 100 100 Salsola kali L. 3 . 25 . . . . . Schoenoplectus lacustris (L.) Palla 3 . . . . . . . Carthamus lanatus L. . . . . . . 33 . Setaria viridis (L.) P. Beauv. 6 . . . . . . . Solanum nigrum L. 3 . . 50 . . 67 100 Sonchus arvensis L. . . . . . . . 50 Sonchus asper (L.) Hill . . . . . . 33 . Sorghum drummondii (Steud.) Millsp. & Chase 3 . . . . . . . Spergularia media (L.) C. Presl. 3 . . . . . . . Tamarix ramosissima Ledeb. 3 . . . . . . 50 Taraxacum besarabicum (Hornem.) Hand.-Mazz. 36 . 50 . . . . . Taraxacum sect. Taraxacum F. H. Wigg. 3 . . . . . 67 50 Teucrium chamaedrys L. 9 . . . . . . . Teucrium polium L. 6 . . . . . . . Trifolium repens L. 3 . . . . . . . Urtica dioica L. 9 . . . . . . 100 Verbascum phlomoides L. 15 . . 100 . . . . Verbena officinalis L. 9 . . . . . 33 100 Veronica serpyllifolia L. . . . . . . . 100 Xanthium orientale subsp. italicum (Moretti) Greuter 18 . . . . . 67 100 Xanthium spinosum L. 24 . . . . . 100 100